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Dataset

 

Global dataset of co-incident TLS-derived and harvested tree biomass

Latest Data Update: 2022-02-08
Status: Completed
Online Status: ONLINE
Publication State: Preview
Publication Date:
THIS RECORD HAS NOT BEEN PUBLISHED YET - PREVIEW ONLY!
Abstract

This dataset contains aboveground biomass estimates generated using terrestrial laser scanning techniques for different species of tree. It was used to produce the figures and statistics of the publication "Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions".

This dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns. Elaborate method descriptions can be found in the publication or in the following publications, which can be found in the documentation sections

Citable as:  [ PROVISIONAL ] Demol, M.; Verbeeck, H.; Gielen, B.; Armston, J.; Burt, A.; Disney, M.; Duncanson, L.; Hackenberg, J.; Kükenbrink, D.; Lau, A.; Ploton, P.; Sewdien, A.; Stovall, A.; Momo Takoudjou, S.; Volkova, L.; Weston, C.; Worte, V.; Calders, K. (9999): Global dataset of co-incident TLS-derived and harvested tree biomass. NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/ddcad11fd71245d3b9d0e669b3fd9169
Abbreviation: Not defined
Keywords: AGB, tls, biomass, trees

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://creativecommons.org/licenses/by/4.0/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).
Data Quality:
Data were validated by the NCEO tls project team
File Format:
Excel spreadsheet

Related Documents

 Burt, A., Boni Vicari, M., da Costa, A. C. L., Coughlin, I., Meir, P., Rowland, L., et al. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science 8, 201458.
 Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., et al. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution 6, 198–208.
 Demol, M., Calders, K., Krishna Moorthy, S. M., Van den Bulcke, J., Verbeeck, H., and Gielen, B. (2021). Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning.
 Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., et al. (2018). Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution 9, 223–234.
 Hackenberg, J., Wassenberg, M., Spiecker, H., and Sun, D. (2015). Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6, 1274–1300
 Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., and Mathys, L. (2021). Aboveground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 1–16
 Lau, A., Calders, K., Bartholomeus, H., Martius, C., Raumonen, P., Herold, M., et al. (2019). Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana. Forests 10, 527
 Momo Takoudjou, S., Ploton, P., Sonke, B., Hackenberg, J., Griffon, S., Coligny, F., et al. (2018). Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods in Ecology and Evolution 9, 905–916
 Stovall, A. E., Vorster, A. G., Anderson, R. S., Evangelista, P. H., and Shugart, H. H. (2017). Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sensing of Environment 200, 31–42.

Process overview

This dataset was generated by instruments deployed on platforms as listed below.
Output Description

None

No variables found.

Coverage
Temporal Range
Start time:
2015-10-01T00:00:00
End time:
2019-02-28T00:00:00
Geographic Extent

 
90.0000°
  
-180.0000°
  
180.0000°
 
-90.0000°
  
Related parties
Authors (18)
Publishers (1)
Funders (1)
Principal Investigators (1)